Text Classification
Transformers
Safetensors
PEFT
branchone
feature-extraction
decision-model
multimodal
custom-code
custom_code
Instructions to use ndemirdemir/BranchOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndemirdemir/BranchOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndemirdemir/BranchOne", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ndemirdemir/BranchOne", trust_remote_code=True, device_map="auto") - PEFT
How to use ndemirdemir/BranchOne with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,080 Bytes
f78d5a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | """Create the pinned inference environment and build its CUDA convolution kernel."""
import argparse
import os
import subprocess
import sys
from pathlib import Path
from cuda_toolchain import configure
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--venv", default=".venv", help="Destination environment")
args = parser.parse_args()
root = Path(__file__).resolve().parent
venv = Path(args.venv).resolve()
configure(install=True)
subprocess.run([sys.executable, "-m", "pip", "install", "uv==0.12.17"], check=True)
subprocess.run(
[sys.executable, "-m", "uv", "sync", "--project", str(root), "--locked",
"--python", "3.13.15"],
env=os.environ | {"UV_PROJECT_ENVIRONMENT": str(venv)}, check=True,
)
subprocess.run(
[str(venv / "bin/python"), str(root / "build_causal_conv.py"),
"--output", str(venv / "kernel-build"), "--install"], check=True,
)
print("Ready. Run examples with " + str(venv / "bin/python"))
if __name__ == "__main__":
main()
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